Toward a Robust Estimation of Respiratory Rate From Pulse Oximeters.

GOAL: Current methods for estimating respiratory rate (RR) from the photoplethysmogram (PPG) typically fail to distinguish between periods of high- and low-quality input data, and fail to perform well on independent "validation" datasets. The lack of robustness of existing methods directly results in a lack of penetration of such systems into clinical practice. The present work proposes an alternative method to improve the robustness of the estimation of RR from the PPG. METHODS: The proposed algorithm is based on the use of multiple autoregressive models of different orders for determining the dominant respiratory frequency in the three respiratory-induced variations (frequency, amplitude, and intensity) derived from the PPG. The algorithm was tested on two different datasets comprising 95 eight-minute PPG recordings (in total) acquired from both children and adults in different clinical settings, and its performance using two window sizes (32 and 64 seconds) was compared with that of existing methods in the literature. RESULTS: The proposed method achieved comparable accuracy to existing methods in the literature, with mean absolute errors (median, 25[Formula: see text]-75[Formula: see text] percentiles for a window size of 32 seconds) of 1.5 (0.3-3.3) and 4.0 (1.8-5.5) breaths per minute (for each dataset respectively), whilst providing RR estimates for a greater proportion of windows (over 90% of the input data are kept). CONCLUSION: Increased robustness of RR estimation by the proposed method was demonstrated. SIGNIFICANCE: This work demonstrates that the use of large publicly available datasets is essential for improving the robustness of wearable-monitoring algorithms for use in clinical practice

The 2023 wearable photoplethysmography roadmap

Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology

Can improved paediatric pneumonia diagnostic aids support frontline health workers in low resource settings? : large scale evaluation of four respiratory rate timers and five pulse oximeters in Cambodia, Ethiopia, South Sudan and Uganda

Background: Pneumonia is the leading cause of infectious death in children under-five in sub-Saharan Africa and Southeast Asia. Currently, the diagnostic criterion for pneumonia is based on increased respiratory rate (RR) in children with cough and/or difficulty breathing. Low oxygen saturation, usually measured using pulse oximeters, is an indication of severe pneumonia. Health workers report finding it difficult to accurately count the number of breaths and current RR counting aids are often difficult to use or unavailable. Improved RR counting aids and lower-cost pulse oximeters are now available but their suitability in these settings and for these populations are untested. Objective: The studies sought to identify and evaluate the most accurate, acceptable and user-friendly respiratory rate counting devices and pulse oximeters for diagnosis of pneumonia symptoms and severity in children by frontline health workers in low-resource settings. Methods: Three sub-studies (I-III) were conducted among health workers, children under five and their caregivers, and national stakeholders. Sub-study I uses an explanatory qualitative approach with pile sorting and focus group discussions with frontline health workers and national stakeholders to explore their perspectives regarding the potential usability and scalability of seven pneumonia diagnostic aids. In sub-study II (a & b) four RR counters and five pulse oximeters were evaluated for performance by a cross-sectional sample of frontline health workers in hospital settings against reference standards in Cambodia, Ethiopia, South Sudan and Uganda. In sub-study III the same nine devices were evaluated using mixed methods for usability and acceptability in routine practice, over three months, in the four countries. Findings: Frontline health workers and national stakeholders’ universally valued device simplicity, affordability and sustainability. They prioritised different device characteristics according to their specific focus of work, with health workers focusing more on device acceptability and national stakeholders’ being less accepting of new technologies (Sub-study I). In sub-study IIa most CHWs managed to achieve a RR count with the four devices. The agreement with the reference standard was low for all; the mean difference of RR measurements or breaths per minute (bpm) from the reference standard for the four devices ranged from 0.5 bpm (95% CI -2.2 to 1.2) for the respirometer to 5.5 bpm (95% CI 3.2 to 7.8) for Rrate. Performance was consistently lower for young infants (0 to <2 months) than for older children (2 to ≤59 months). Agreement of RR classification into fast and normal breathing was moderate across all four devices, with Cohen’s Kappa statistics ranging from 0.41 (SE 0.04) to 0.49 (SE 0.05). In Sub-study IIb, although all five pulse oximeters tested in the field had performed well on a simulator (±2% SpO2 from the simulator), their performance was more varied when used on real children by frontline health workers. The handheld pulse oximeters had greater overall agreement with the reference standard, ranging from -0.6% SpO2 (95% CI -0.9, 0.4) to -3.0% SpO2 (95% CI -3.4, -2.6) than the finger-tip pulse oximeters, which ranged from -3.9% SpO2 (95% CI -4.4, -3.4) to -7.9% SpO2 (95% CI -8.6,-7.2). This was particularly pronounced in the younger children, where handheld devices had -0.7 SpO2 (95% CI -1.4, -0.1) to -5.9 SpO2 (95% CI -6.9, -4.9) agreement, compared to fingertip devices, which had -8.0 SpO2 (95% CI -9.4, -6.6) to -13.3 SpO2 (95% CI -15.1, -11.5) agreement. First level health facility workers had better agreement in classification of hypoxaemia with the reference standard (=0.32; SE 0.05 to =0.86; SE 0.07) for all five devices, when compared to CHWs (=0.15; SE 0.02 to =0.59; SE 0.03). In Sub-study III health workers reported being better supported by assisted RR counters, which provided more support than their standard practice ARI timer in counting and classifying RR in sick children under 5 in these settings. Conclusions: Frontline health workers were able to use the nine test devices to measure RR and oxygen saturation in children under 5, but with variable performance, and found it more difficult to get a successful measurement in younger children. Frontline health workers were better supported by assisted RR counters, such as Rrate and respirometer, compared to their standard practice diagnostic aid, MK2 ARI timer. Handheld pulse oximeters with multiple probes performed better than fingertip pulse oximeters, especially in younger children. The views of different stakeholder groups should be considered when looking to take these types of pneumonia diagnostic aids to scale. A consensus view on a robust research method and reference standard to evaluate future pneumonia diagnostic aids needs to be reached. While laboratory testing of new diagnostic aids can be valuable it should not replace field testing with frontline health workers in routine practice. Automated, easy to use, robust and affordable pneumonia diagnostics aids need to be developed and launched at scale to better support frontline health workers to address the high pneumonia burden in resource poor settings

The striking need for age diverse pulse oximeter databases

ISSN:2296-858

The Challenges and Pitfalls of Detecting Sleep Hypopnea Using a Wearable Optical Sensor: Comparative Study.

BACKGROUND Obstructive sleep apnea (OSA) is the most prevalent respiratory sleep disorder occurring in 9% to 38% of the general population. About 90% of patients with suspected OSA remain undiagnosed due to the lack of sleep laboratories or specialists and the high cost of gold-standard in-lab polysomnography diagnosis, leading to a decreased quality of life and increased health care burden in cardio- and cerebrovascular diseases. Wearable sleep trackers like smartwatches and armbands are booming, creating a hope for cost-efficient at-home OSA diagnosis and assessment of treatment (eg, continuous positive airway pressure [CPAP] therapy) effectiveness. However, such wearables are currently still not available and cannot be used to detect sleep hypopnea. Sleep hypopnea is defined by ≥30% drop in breathing and an at least 3% drop in peripheral capillary oxygen saturation (Spo2) measured at the fingertip. Whether the conventional measures of oxygen desaturation (OD) at the fingertip and at the arm or wrist are identical is essentially unknown. OBJECTIVE We aimed to compare event-by-event arm OD (arm_OD) with fingertip OD (finger_OD) in sleep hypopneas during both naïve sleep and CPAP therapy. METHODS Thirty patients with OSA underwent an incremental, stepwise CPAP titration protocol during all-night in-lab video-polysomnography monitoring (ie, 1-h baseline sleep without CPAP followed by stepwise increments of 1 cmH2O pressure per hour starting from 5 to 8 cmH2O depending on the individual). Arm_OD of the left biceps muscle and finger_OD of the left index fingertip in sleep hypopneas were simultaneously measured by frequency-domain near-infrared spectroscopy and video-polysomnography photoplethysmography, respectively. Bland-Altman plots were used to illustrate the agreements between arm_OD and finger_OD during baseline sleep and under CPAP. We used t tests to determine whether these measurements significantly differed. RESULTS In total, 534 obstructive apneas and 2185 hypopneas were recorded. Of the 2185 hypopneas, 668 (30.57%) were collected during baseline sleep and 1517 (69.43%), during CPAP sleep. The mean difference between finger_OD and arm_OD was 2.86% (95% CI 2.67%-3.06%, t667=28.28; P<.001; 95% limits of agreement [LoA] -2.27%, 8.00%) during baseline sleep and 1.83% (95% CI 1.72%-1.94%, t1516=31.99; P<.001; 95% LoA -2.54%, 6.19%) during CPAP. Using the standard criterion of 3% saturation drop, arm_OD only recognized 16.32% (109/668) and 14.90% (226/1517) of hypopneas at baseline and during CPAP, respectively. CONCLUSIONS arm_OD is 2% to 3% lower than standard finger_OD in sleep hypopnea, probably because the measured arm_OD originates physiologically from arterioles, venules, and capillaries; thus, the venous blood adversely affects its value. Our findings demonstrate that the standard criterion of ≥3% OD drop at the arm or wrist is not suitable to define hypopnea because it could provide large false-negative results in diagnosing OSA and assessing CPAP treatment effectiveness

Machine learning algorithm development of SPO2 sensor for improved robustness in wearables

Wearable devices application in the digital measurement of health has gained attention by researchers. These devices allow for data acquisition during real-life activities, resulting in higher data availability. They often include photoplethysmography (PPG) sensors, the sensor behind pulse oximetry. Pulse oximetry is a non-invasive method for continuous oxygen saturation (SpO2) measurements, a standard monitor for anesthesia procedures, an essential tool for managing patients undergoing pulmonary rehabilitation and an effective method for assessing sleep-disordered breathing. However, the current market focuses on heart rate measurements and lacks the robustness of clinical applications for SpO2 assessment. In addition, the most common obstacle in PPG measurements is the signal quality, especially in the form of motion artifacts. Thus, this work aims at increasing the clinical robustness in this devices by evaluating its quality and then extracting relevant metrics. Firstly, a data acquisition protocol was developed, focused on acquiring data during daily activities. This resulted in a dataset with different signal qualities, which was manually annotated to be used as the base for the Machine Learning models. A second protocol was also developed especially designed for the extraction of the SpO2 measurement. Several Machine Learning models were developed to evaluate the signal in three distinct qualities (corrupted, suboptimal, optimal) in real time. A Random Forest classifier achieved accuracies of 79% and 80% for the binary models capable of differentiating between usable and unusable signals, and accuracies of 74% and 80% when distinguishing between optimal and suboptimal signals, for the two utilized channels. The multi-class models achieved accuracies of 66% and 65% for the two utilized channels. Three clinically relevant metrics were also extracted from the PPG signal: heart rate, respiratory rate and SpO2. The heart rate and respiratory rate algorithms resulted in performances similar to the ones found in the literature and in other devices currently on the market. However, while promising, more data is needed to reach statistical significance for the SpO2 measurement.A monitorização do estado de saúde de pacientes em ambulatório utilizando dispositivos wearables tem vindo a ser cada vez mais investigada. Estes dispositivos permitem uma aquisição de dados durante o dia a dia, resultando num maior conjunto de dados. Frequentemente, estes dispositivos incluem fotopletismógrafos (PPG), o sensor por detrás da oximetria de pulso. A oximetria de pulso é um método não invasivo para a medição da saturação de oxigénio no sangue (SpO2) de forma contínua. É um equipamento padrão para procedimentos com anestesia, uma ferramenta essencial para monitorizar pacientes em reabilitação pulmonar e um método eficaz para avaliar respiração desordenada do sono. Ainda assim, o mercado atual foca-se principalmente em medições da frequência cardíaca e carece robustez para aplicações clínicas da medição de SpO2. Para além disso, o obstáculo mais comum em medições com PPG é a qualidade do sinal. Consequentemente, este trabalho procura melhorar a robustez clínica destes dispositivos analisando a qualidade do sinal e, posteriormente, extrair métricas relevantes. Primeiramente, foi desenvolvido um protocolo para aquisição de dados de atividades do dia a dia. Assim, foram adquiridos dados com diferentes qualidades, que foram avaliados manualmente de forma a servir de base para os vários modelos de Machine Learning. Também foi desenvolvido um segundo protocolo para a extração do valor de SpO2. Diferentes modelos de Machine Learning foram desenvolvidos para avaliar em tempo real a qualidade do sinal em três qualidades (corrompido, subótimo, ótimo) . Um classificador baseado em Random Forest atingiu exatidões de 79% e 80% em classificadores binários capazes de distinguir entre sinais úteis e inúteis, e exatidões de 74% e 80% a diferenciar entre um sinal subótimo e ótimo, para os dois canais usados. Os classificadores multi-classe atingiram exatidões de 66% e 65% para os dois canais usados. Três medidas clinicamente relevantes foram também extraídas do sinal de PPG: frequências cardíaca e respiratória, cujos algoritmos atingiram resultados semelhantes aos encontrados na literatura e em aparelhos no mercado, e SpO2 que, ainda que promissores, mais dados seriam necessários para os resultados serem estatisticamente significativo